Research Paper | Computer Science & Engineering | India | Volume 7 Issue 8, August 2018
Stock Price Movement Prediction using Attention-Based Neural Network Framework
Kartik Goyal  | Nitin Bansal | Soumyabrata Kundu | Ayan Kundu | Nitish Jain
Abstract: There is a lot of scientific work going on NLP trying to predict the impact of news on a stock price, much of this uses basic features (such as bags-of-words, named entities etc. ), but fails to capture structured entity-relation, and hence lacks accuracy.1. Encoding the information like daily events, meta-stock information and stocks 50 days moving average using LSTM.2. Employing attention mechanism to rate the relevancy of all events for each stock.3. Using non-linear neural network on the weighted events to predict the stock movement. The model achieved an accuracy of around 72 % on test set.
Keywords: Stock Price Movement, Neural Network, Attention Mechanism, NLP
Edition: Volume 7 Issue 8, August 2018,
Pages: 7 - 9
How to Cite this Article?
Kartik Goyal, Nitin Bansal, Soumyabrata Kundu, Ayan Kundu, Nitish Jain, "Stock Price Movement Prediction using Attention-Based Neural Network Framework", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=ART2019326, Volume 7 Issue 8, August 2018, 7 - 9, #ijsrnet
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